River Water Quality Zoning Using Combination of Principal Component Analysis (PCA) and Fuzzy Clustering Analysis

Document Type : Research Paper


Assoc. Prof., Isfahan University of Tech.


Management decisions whose environmental impacts affect directly or indirectly surface waters must of necessity be based on adequate knowledge and information when water quality zoning and a clear picture of river water quality are sought. Water quality zoning is based on pollution criteria that are identified on the basis of different water quality parameters drawn from historical data and the water uses in the region. The aggregate of the data and parameters involved make river water quality modeling a complex process. In this paper, the Principal Component Analysis (PCA) is used to reduce the water quality parameters involved in the identification of river water pollution criteria. The method keeps those components with more variances. The results show that the first component transfers 93.59% of the variation in the data, while the first two and the first six components explain 96.67% and 99.99% of the variations, respectively. Based on the criteria thus identified, the fuzzy clustering analysis is used in a second stage of the study to classify the river intervals. For this purpose, the fuzzy water quality data are provided to generate the fuzzy similarity matrix based on the fuzzy relations. Then, the stabilized matrix and the clustering diagram are created. Finally, the river intervals are classified into similar categories using the proper thresholds. The efficiency of the proposed method is evaluated by employing water quality data collected from the Zayandehrood River monitoring stations.


Main Subjects

1. Shamsaei, A., Oreei Zareh, S., and Sarang, A. (2005). “The comparison of water indices and zoning quality in karoon and Dez rivers.” J. of Water and Wastewater, 55, 39-48. (In Persian)
2. Assadollah Fardi, Gh. (2003). “Review of water quality indices and their application to the karoon river.” M.Sc. Thesis, Dept. of Civil Eng., Iran University of Science and Tech., Tehran. (In Persian)
3.Horton, R. K. (1965). “An index-number system for rating water quality.” Water PollutionControl Fed., 37, 300-306.
4. Ott, W.R. (1978). Environmental indices -Theory and practices, Ann Arbor Science Publishers Inc., Michigan, USA.
5. National Sanitation Foundation (NSF). (2003). <http://www.Nsfconsumer.rg/environment/ wqi.asp.>
6. Asgari, H., and Kerachian, R. (2006). “River water quality zoning by SVMS.” 2nd National Conf. of Iran Water Resources Management, Isfahan. (In Perisian)
7. Verma, A.K., and Singh, T.N. (2013). “Prediction of water quality from simple field parameters.” Environ. Earth Sci., 69(3), 821-829.
8. Facchinelli, A., Sacchi, E., and Mallen, L. (2001). “Multivariate statistical and GIS-based approach to identify heavy metals sources in soils.” Environmental Pollution, 114, 313-324.
9. Gangopadhyay, S., Das Gupta, A., and Nachabe, M.H. (2001). “Evaluation of groundwater monitoring network by principal component analysis.” Groundwater, 39(2), 181-191.
10. Giussani, B. (2008). “Three-way principal component analysis of chemical data from Lake Como watershed.” Microchemical Journal, 88, 160-166.
11. Iscen, C., Emiroglu, O., Ilhan, S., Arslan, N., Yilmaz, V., and Ahiska, S. (2008). “Application of multivariate statistical techniques in the assessment of surface water quality in Uluabat Lake.” Turkey Environ. Monit. Assess., 144, 269-276.
12. Oueslati, O., Maria, A., Girolamo, D., Abouabdillah, A., and Porto, A. (2010). “Attempts to flow regime classification and characterization in Mediterranean streams using multivariate.” International Workshop in Statistical Hydrology, Taormina, Italy.
13. Noori, R., Kerachian, R., Khodadadi Darban, A., and Shakibaienia, A. (2007). “Assessment of improtance of water quality monitoring stations using  principal components analysis and factor analysis : A case study of the Karoon river.” J. of Water and Wastewater, 63, 60-69. (In Persian)
14. Mishra, A. (2010). “Assessment of water quality using principal component analysis: A case study of the river Ganges.” J. of Water Chemistry and Technology, 32(4), 227-234.
15. Noori, R., Sabahi, M.S., Karbassi, A.R., Baghvand, A., and Taati Zadeh, H. (2010). “Multivariate statistical analysis of surface water quality based on correlations and variations in the data set.” Desalination, 260, 129-136.
16. Singh, K.P., Basant, A., Malik, A., and Jain, G. (2009). “Artificial neural network modeling of the river water quality, a case study.” Ecol. Model, 220, 888-895.
17. Talib, A., and Amat, M.I. (2012). “Prediction of chemical oxygen demand in Dondang river using artificial neural network.” Int. J. Inf. Educt. Technol., 2(3), 259-261.
18. Ranković, V., Radulović, J., Radojević, I., Ostojić, A., and Čomić, L. (2012). “Prediction of dissolved oxygen in reservoirs using adaptive network-based fuzzy inference system.” J. Hydroinform, 14(1),167-179.
19. Zadeh, L. A. (1965). “Fuzzy sets.” Information and Control, 8, 338-353.
20. Zadeh, L. A. (1973). “Outline of a new approach to the analysis of a new systems and design process.” IEEE Trans, SMC, 3, 28-44.
21. Kung, H. T., Ying, L. G., and Liu, Y. C. (1992). “A complementary tool to water quality index: Fuzzy clustering analysis.” Water Resources Bulletin, 28, 525-533.
22. Noroozian, K., Tajrishy, M., and Abrishamchi, A. (2001). “River water quality zoning using fuzzy classification, case study: Zayanderood river.” Esteghlal, 20 (1), 55-68. (In Persian)
23. Karamouz, M., Mahjouri, N., and Kerachian, R. (2004). “River water quality zoning: A case study of Karoon-Dez river system.” Iranian J. Public Health Science and Engineering, 1(2), 16-27.
24. Nikoo, M., and Kerachian, R. (2010). “Surface water quality zoning by Bayesian SVM.”4th National Conf. of Environmental Engineering, Tehran. (In Persian)
25. Razmkhah, H., Abrishamchi, A., and Torkian, A. (2010). “Evaluation of spatial and temporal variation in water quality by pattern recognition techniques: A case study on Jajrood river (Tehran, Iran).” J. of Environmental Management, 91, 852-860.
27. Wang, P. (1983). Approaching degree method, in Fuzzy sets theory and its applications, Science and Technology Press, Shanghai, PRC.
28. Van der Leen, F., Troise, F. L., and Todd, K. D., (1990). The water encyclopedia, 2nd Ed., Lewis Publisher, USA.